Efficient fuzzy-connectedness segmentation using symmetric convolution and adaptive thresholding

Fuzzy Connectedness segmentation emerged in recent years as an alternative to traditional "hard" image-segmentation approaches. It employs scale-based affinity, which incorporates both fuzziness and degree of hanging-togetherness of a region, to extract regions of interest from, especially, medical images. Computation complexity has been, however, one of its arguable issues that needs further theoretical investigation and improvement. Furthermore, the homogeneity parameter needs to be specified on per image fashion. In this paper we propose an improved fuzzy connectedness segmentation method by utilizing a sequential grow-and-merge scheme that we called symmetric convolution and an adaptive thresholding technique that incorporates an entropy-guided process to determine the homogeneity parameter. The proposed approach with symmetric convolution is proven valid and efficient. We employ a simulated on-line Brain database-BrainWeb to generate the testbed to evaluate the accuracy and robustness of the proposed algorithm.